def add_convenience_annotations(annotation):
"""
Add a bunch of convenience lookups to an annotation.
This is kind of a historical relic - should try to remove as many as we can
TODO: yeah let's aim to get rid of this completely
"""
vep_annotation = annotation['vep_annotation']
annotation['gene_ids'] = vep_annotations.get_gene_ids(vep_annotation)
annotation["coding_gene_ids"] = vep_annotations.get_coding_gene_ids(vep_annotation)
annotation['worst_vep_annotation_index'] = vep_annotations.get_worst_vep_annotation_index(vep_annotation)
annotation['worst_vep_index_per_gene'] = {}
annotation['annotation_tags'] = list({a['consequence'] for a in vep_annotation})
for gene_id in annotation['gene_ids']:
annotation['worst_vep_index_per_gene'][gene_id] = vep_annotations.get_worst_vep_annotation_index(
vep_annotation,
gene_id=gene_id
)
per_gene = {}
for gene_id in annotation['coding_gene_ids']:
per_gene[gene_id] = vep_annotations.get_worst_vep_annotation_index(vep_annotation, gene_id=gene_id)
annotation['worst_vep_index_per_gene'] = per_gene
worst_vep_annotation = vep_annotation[annotation['worst_vep_annotation_index']]
annotation['vep_consequence'] = None
if worst_vep_annotation:
annotation['vep_consequence'] = worst_vep_annotation['consequence']
annotation['vep_group'] = None
if worst_vep_annotation:
annotation['vep_group'] = constants.ANNOTATION_GROUP_REVERSE_MAP[annotation['vep_consequence']]
def add_convenience_annotations(annotation):
"""
Add a bunch of convenience lookups to an annotation.
This is kind of a historical relic - should try to remove as many as we can
TODO: yeah let's aim to get rid of this completely
"""
vep_annotation = annotation['vep_annotation']
annotation['gene_ids'] = vep_annotations.get_gene_ids(vep_annotation)
annotation["coding_gene_ids"] = vep_annotations.get_coding_gene_ids(vep_annotation)
annotation['worst_vep_annotation_index'] = vep_annotations.get_worst_vep_annotation_index(vep_annotation)
annotation['worst_vep_index_per_gene'] = {}
annotation['annotation_tags'] = list({a['consequence'] for a in vep_annotation})
for gene_id in annotation['gene_ids']:
annotation['worst_vep_index_per_gene'][gene_id] = vep_annotations.get_worst_vep_annotation_index(
vep_annotation,
gene_id=gene_id
)
per_gene = {}
for gene_id in annotation['coding_gene_ids']:
per_gene[gene_id] = vep_annotations.get_worst_vep_annotation_index(vep_annotation, gene_id=gene_id)
annotation['worst_vep_index_per_gene'] = per_gene
worst_vep_annotation = vep_annotation[annotation['worst_vep_annotation_index']]
annotation['vep_consequence'] = None
if worst_vep_annotation:
annotation['vep_consequence'] = worst_vep_annotation['consequence']
annotation['vep_group'] = None
if worst_vep_annotation:
annotation['vep_group'] = constants.ANNOTATION_GROUP_REVERSE_MAP[annotation['vep_consequence']]
def test_get_worst_vep_annotation_index(self):
annotations = [
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000479049', 'Consequence': 'non_coding_transcript_exon_variant', 'Protein_position': '', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000258104', 'Consequence': 'stop_gained', 'Protein_position': '1968', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000394120', 'Consequence': 'stop_gained', 'Protein_position': '1969', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000409366', 'Consequence': 'stop_gained', 'Protein_position': '1990', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000409582', 'Consequence': 'stop_gained', 'Protein_position': '2006', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000409651', 'Consequence': 'stop_gained', 'Protein_position': '2000', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000409744', 'Consequence': 'stop_gained', 'Protein_position': '1976', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000409762', 'Consequence': 'stop_gained', 'Protein_position': '1985', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000410020', 'Consequence': 'stop_gained', 'Protein_position': '2007', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': 'YES'},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000410041', 'Consequence': 'stop_gained', 'Protein_position': '1986', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000413539', 'Consequence': 'stop_gained', 'Protein_position': '1999', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
{'Feature_type': 'Transcript', 'biotype': 'other', 'GMAF': '', 'Feature': 'ENST00000429174', 'Consequence': 'stop_gained', 'Protein_position': '1989', 'Gene': 'ENSG00000135636', 'STRAND': '1', 'CANONICAL': ''},
]
# convert keys to lower case
for annot_dict in annotations:
for key, value in annot_dict.items():
annot_dict[key.lower()] = value
annot_dict['is_nc'] = False
annot_dict['is_nmd'] = False
# test basic case
self.assertEqual(get_worst_vep_annotation_index(annotations), 8)
# test 2 annotations being canonical - choose the worst one
annotations[0]['canonical'] = 'YES'
self.assertEqual(get_worst_vep_annotation_index(annotations), 8)
# test 0 annotations being canonical - choose the worst one
annotations[0]['canonical'] = ''
annotations[8]['canonical'] = ''
i = get_worst_vep_annotation_index(annotations)
self.assertTrue(annotations[i]['consequence'], 'stop_gained')
self.assertEqual(annotations[i]['feature'], 'ENST00000258104')
# test where worst-affected transcript is not the canonical one
annotations[1]['consequence'] = 'splice_donor_variant'
self.assertEqual(get_worst_vep_annotation_index(annotations), 1)
self.assertFalse(annotations[1]['canonical'])
# test protein coding filter
annotations[6]['biotype'] = 'protein_coding'
self.assertEqual(get_worst_vep_annotation_index(annotations), 6)
# test the gene_id arg
annotations[8]['canonical'] = 'YES'
annotations[1]['gene'] = 'OTHER_GENE1'
annotations[2]['gene'] = 'OTHER_GENE2'
self.assertEqual(get_worst_vep_annotation_index(annotations, gene_id='OTHER_GENE1'), 1)
self.assertEqual(get_worst_vep_annotation_index(annotations, gene_id='OTHER_GENE2'), 2)
annotations[8]['canonical'] = 'NO'
def get_output_row(self,
variant,
xpos,
ref,
alt,
individual_id,
family,
all_fields=False,
comments="",
gene_id=""):
v = variant
if individual_id not in v.genotypes:
print("skipping variant: %s because individual %s not in %s" %
(str(xpos) + " " + ref + ">" + alt, individual_id,
family.family_id))
return None
gene_id = gene_id.split(
"."
)[0] if gene_id else None # strip off the gene_id suffix (eg. '.3')
genotype = v.genotypes[individual_id]
if genotype.gq is None:
print(
"skipping variant: %s because this variant is not called in this individual (%s)"
% (str(xpos) + " " + ref + ">" + alt,
individual_id)) #, str(genotype)))
return None
chrom, pos = genomeloc.get_chr_pos(xpos)
chrom_without_chr = chrom.replace("chr", "")
annot = v.annotation
if gene_id:
worst_vep_annotation_index = vep_annotations.get_worst_vep_annotation_index(
annot["vep_annotation"], gene_id=gene_id)
else:
# create dictionary that maps gene id to the index of the worst vep annotation for that gene
protein_coding_gene_ids = set(a['gene']
for a in annot["vep_annotation"]
if a['biotype'] == 'protein_coding')
if not protein_coding_gene_ids:
print(
"skipping variant %s in this individual (%s) because none of the transcripts are protein coding: %s"
%
(str(xpos) + " " + ref + ">" + alt, individual_id, annot))
return None
worst_vep_annotation_index = vep_annotations.get_worst_vep_annotation_index(
annot["vep_annotation"], gene_id=protein_coding_gene_ids)
if len(protein_coding_gene_ids) > 1:
selected_gene_id = annot["vep_annotation"][
worst_vep_annotation_index]['gene']
print("Selected %s from %s" %
(annot["vep_annotation"][worst_vep_annotation_index]
['symbol'],
set([
a['symbol'] for a in annot["vep_annotation"]
if a['gene'] in protein_coding_gene_ids
])))
vep = annot["vep_annotation"][
worst_vep_annotation_index] # ea_maf, swissprot, existing_variation, pubmed, aa_maf, ccds, high_inf_pos, cdna_position, canonical, tsl, feature_type, intron, trembl, feature, codons, polyphen, clin_sig, motif_pos, protein_position, afr_maf, amino_acids, cds_position, symbol, uniparc, eur_maf, hgnc_id, consequence, sift, exon, biotype, is_nc, gmaf, motif_name, strand, motif_score_change, distance, hgvsp, ensp, allele, symbol_source, amr_maf, somatic, hgvsc, asn_maf, is_nmd, domains, gene
worst_vep_annotation_index = vep_annotations.get_worst_vep_annotation_index(
annot["vep_annotation"])
vep = annot["vep_annotation"][worst_vep_annotation_index]
if "symbol" in vep and "consequence" in vep:
gene_name = vep["symbol"] # vep["gene"]
functional_class = vep["consequence"]
else:
gene_name = functional_class = ""
print(
"ERROR: gene_name and functional_class not found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s"
% locals())
if genotype.num_alt is None:
s = "\n\n"
for i, g in v.genotypes.items():
s += str(i) + ": " + str(g) + "\n"
raise ValueError("genotype.num_alt is None: " + str(genotype) +
"\n" + str(v.toJSON()) + "\n" + s)
genotype_str = genotype_map[genotype.num_alt]
variant_str = "%s:%s %s>%s" % (chrom, pos, ref, alt)
if "hgvsc" in vep and "hgvsp" in vep:
#print("hgvs_c and/or hgvs_p WAS found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s" % locals())
hgvs_c = urllib.unquote(vep["hgvsc"])
hgvs_p = urllib.unquote(vep["hgvsp"])
else:
hgvs_c = hgvs_p = ""
#print("ERROR: hgvs_c and/or hgvs_p not found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s" % locals())
rsid = annot["rsid"] or ""
#rsid = vep["clinvar_rs"]
exac_global_af, exac_popmax_af, exac_popmax_population = get_exac_af(
chrom, pos, ref, alt)
if exac_global_af is None:
exac_global_af, exac_popmax_af, exac_popmax_population = 0, 0, "[variant not found in ExACv0.3]"
else:
exac_global_af_annot = str(annot["freqs"]["exac_v3"])
if abs(float(exac_global_af) - float(exac_global_af_annot)) > 0.01:
print(
"Error annot['freqs']['exac_v3'] (%s) doesn't match %s" %
(float(exac_global_af), float(exac_global_af_annot)))
clinvar_clinsig = ""
clinvar_clnrevstat = ""
if "clin_sig" in vep:
clinvar_clinsig_from_dbnsfp = vep["clin_sig"]
else:
clinvar_clinsig_from_dbnsfp = ""
#print("ERROR: clin_sig not found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s" % locals())
clinvar_records = [
record
for record in clinvar_vcf_file.fetch(chrom_without_chr, pos, pos)
if record.POS == pos and record.REF == ref
]
#if clinvar_clinsig_from_dbnsfp or clinvar_records:
# defensive programming
#if clinvar_clinsig_from_dbnsfp and not clinvar_records:
# raise ValueError("record has dbNSFP clinvar entry but is not in clinvar vcf: %s" % variant_str)
#if not clinvar_clinsig_from_dbnsfp and clinvar_records:
# raise ValueError("record doesn't have a dbNSFP clinvar entry but is in clinvar vcf: %s" % variant_str)
if clinvar_records:
#if len(clinvar_records) > 1:
# raise ValueError("multiple clinvar records found for variant: %s" % variant_str)
clinvar_record = clinvar_records[-1]
clinvar_allele_indexes = map(int, clinvar_record.INFO["CLNALLE"])
clinvar_alleles = map(str,
[clinvar_record.REF] + clinvar_record.ALT)
xbrowse_alleles = map(str, [ref] + [alt])
clinvar_value_indexes_to_use = [
i for i, clinvar_allele_index in enumerate(
clinvar_allele_indexes)
if str(clinvar_alleles[clinvar_allele_index]).upper() in
xbrowse_alleles
]
clnrevstat = clinvar_record.INFO["CLNREVSTAT"]
clnrevstat = [clnrevstat[i] for i in clinvar_value_indexes_to_use]
clnsig = clinvar_record.INFO["CLNSIG"]
clnsig = [clnsig[i] for i in clinvar_value_indexes_to_use]
# print("Fetched clinvar %s: %s"% (clinvar_record, clinvar_record.INFO))
if clnsig:
clinvar_clinsig_numbers = map(int, clnsig[0].split("|"))
clinvar_clinsig = "|".join(
set([
clinsig_map[clinvar_clinsig_number][0]
for clinvar_clinsig_number in clinvar_clinsig_numbers
]))
clinvar_clnrevstat = "|".join(set(clnrevstat[0].split("|")))
# get
number_of_stars = "[not found]" if all_fields else "[not retrieved to save time]"
clinvar_url = "http://www.ncbi.nlm.nih.gov/clinvar/?term=" + chrom_without_chr + "[chr]+AND+" + str(
pos) + "[chrpos37]"
if clinvar_clinsig and all_fields:
print("Reading from: " + clinvar_url)
url_opener = urllib2.build_opener()
url_opener.addheaders = [(
'User-agent',
"Mozilla/5.0 (X11; U; Linux i686) Gecko/20071127 Firefox/2.0.0.11"
)]
page_contents = url_opener.open(clinvar_url).read()
match = re.search("(\d) star.? out of maximum of 4 stars",
page_contents)
if match:
number_of_stars = int(match.group(1))
else:
print("No match in page: " + clinvar_url)
for line in page_contents.split("\n"):
if "rev_stat_text hide" in line:
print(
" -- this line was expected to contain number of stars: "
+ line)
row = map(str, [
gene_name, genotype_str, variant_str, functional_class, hgvs_c,
hgvs_p, rsid, exac_global_af, exac_popmax_af,
exac_popmax_population, clinvar_clinsig, clinvar_clnrevstat,
number_of_stars, clinvar_url, comments
])
return row
def get_output_row(self, variant, xpos, ref, alt, individual_id, family, all_fields=False, comments="", gene_id=""):
v = variant
if individual_id not in v.genotypes:
print("skipping variant: %s because individual %s not in %s" % (str(xpos) + " " + ref + ">" + alt, individual_id, family.family_id))
return None
gene_id = gene_id.split(".")[0] if gene_id else None # strip off the gene_id suffix (eg. '.3')
genotype = v.genotypes[individual_id]
if genotype.gq is None:
print("skipping variant: %s because this variant is not called in this individual (%s)" % (str(xpos)+" " + ref + ">" + alt, individual_id)) #, str(genotype)))
return None
chrom, pos = genomeloc.get_chr_pos(xpos)
chrom_without_chr = chrom.replace("chr", "")
annot = v.annotation
if gene_id:
worst_vep_annotation_index = vep_annotations.get_worst_vep_annotation_index(annot["vep_annotation"], gene_id = gene_id)
else:
# create dictionary that maps gene id to the index of the worst vep annotation for that gene
protein_coding_gene_ids = set(a['gene'] for a in annot["vep_annotation"] if a['biotype'] == 'protein_coding')
if not protein_coding_gene_ids:
print("skipping variant %s in this individual (%s) because none of the transcripts are protein coding: %s" % (str(xpos)+" " + ref + ">" + alt, individual_id, annot))
return None
worst_vep_annotation_index = vep_annotations.get_worst_vep_annotation_index(annot["vep_annotation"], gene_id=protein_coding_gene_ids)
if len(protein_coding_gene_ids) > 1:
selected_gene_id = annot["vep_annotation"][worst_vep_annotation_index]['gene']
print("Selected %s from %s" % (annot["vep_annotation"][worst_vep_annotation_index]['symbol'], set([a['symbol'] for a in annot["vep_annotation"] if a['gene'] in protein_coding_gene_ids])))
vep = annot["vep_annotation"][worst_vep_annotation_index] # ea_maf, swissprot, existing_variation, pubmed, aa_maf, ccds, high_inf_pos, cdna_position, canonical, tsl, feature_type, intron, trembl, feature, codons, polyphen, clin_sig, motif_pos, protein_position, afr_maf, amino_acids, cds_position, symbol, uniparc, eur_maf, hgnc_id, consequence, sift, exon, biotype, is_nc, gmaf, motif_name, strand, motif_score_change, distance, hgvsp, ensp, allele, symbol_source, amr_maf, somatic, hgvsc, asn_maf, is_nmd, domains, gene
worst_vep_annotation_index = vep_annotations.get_worst_vep_annotation_index(annot["vep_annotation"])
vep = annot["vep_annotation"][worst_vep_annotation_index]
if "symbol" in vep and "consequence"in vep:
gene_name = vep["symbol"] # vep["gene"]
functional_class = vep["consequence"]
else:
gene_name = functional_class = ""
print("ERROR: gene_name and functional_class not found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s" % locals())
if genotype.num_alt is None:
s = "\n\n"
for i, g in v.genotypes.items():
s += str(i) + ": " + str(g) + "\n"
raise ValueError("genotype.num_alt is None: " + str(genotype) + "\n" + str(v.toJSON()) + "\n" + s)
genotype_str = genotype_map[genotype.num_alt]
variant_str = "%s:%s %s>%s" % (chrom, pos, ref, alt)
if "hgvsc" in vep and "hgvsp"in vep:
#print("hgvs_c and/or hgvs_p WAS found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s" % locals())
hgvs_c = urllib.unquote(vep["hgvsc"])
hgvs_p = urllib.unquote(vep["hgvsp"])
else:
hgvs_c = hgvs_p = ""
#print("ERROR: hgvs_c and/or hgvs_p not found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s" % locals())
rsid = annot["rsid"] or ""
#rsid = vep["clinvar_rs"]
exac_global_af, exac_popmax_af, exac_popmax_population = get_exac_af(chrom, pos, ref, alt)
if exac_global_af is None:
exac_global_af, exac_popmax_af, exac_popmax_population = 0, 0, "[variant not found in ExACv0.3]"
else:
exac_global_af_annot = str(annot["freqs"]["exac_v3"])
if abs(float(exac_global_af) - float(exac_global_af_annot)) > 0.01:
print("Error annot['freqs']['exac_v3'] (%s) doesn't match %s" % (float(exac_global_af), float(exac_global_af_annot)))
clinvar_clinsig = ""
clinvar_clnrevstat = ""
if "clin_sig" in vep:
clinvar_clinsig_from_dbnsfp = vep["clin_sig"]
else:
clinvar_clinsig_from_dbnsfp = ""
#print("ERROR: clin_sig not found in annot['vep_annotation'][annot['worst_vep_annotation_index']]: %(vep)s" % locals())
clinvar_records = [record for record in clinvar_vcf_file.fetch(chrom_without_chr, pos, pos) if record.POS == pos and record.REF == ref]
#if clinvar_clinsig_from_dbnsfp or clinvar_records:
# defensive programming
#if clinvar_clinsig_from_dbnsfp and not clinvar_records:
# raise ValueError("record has dbNSFP clinvar entry but is not in clinvar vcf: %s" % variant_str)
#if not clinvar_clinsig_from_dbnsfp and clinvar_records:
# raise ValueError("record doesn't have a dbNSFP clinvar entry but is in clinvar vcf: %s" % variant_str)
if clinvar_records:
#if len(clinvar_records) > 1:
# raise ValueError("multiple clinvar records found for variant: %s" % variant_str)
clinvar_record = clinvar_records[-1]
clinvar_allele_indexes = map(int, clinvar_record.INFO["CLNALLE"])
clinvar_alleles = map(str, [clinvar_record.REF] + clinvar_record.ALT)
xbrowse_alleles = map(str, [ref] + [alt])
clinvar_value_indexes_to_use = [i for i, clinvar_allele_index in enumerate(clinvar_allele_indexes) if str(clinvar_alleles[clinvar_allele_index]).upper() in xbrowse_alleles]
clnrevstat = clinvar_record.INFO["CLNREVSTAT"]
clnrevstat = [clnrevstat[i] for i in clinvar_value_indexes_to_use]
clnsig = clinvar_record.INFO["CLNSIG"]
clnsig = [clnsig[i] for i in clinvar_value_indexes_to_use]
# print("Fetched clinvar %s: %s"% (clinvar_record, clinvar_record.INFO))
if clnsig:
clinvar_clinsig_numbers = map(int, clnsig[0].split("|"))
clinvar_clinsig = "|".join(set([clinsig_map[clinvar_clinsig_number][0] for clinvar_clinsig_number in clinvar_clinsig_numbers]))
clinvar_clnrevstat = "|".join(set(clnrevstat[0].split("|")))
# get
number_of_stars = "[not found]" if all_fields else "[not retrieved to save time]"
clinvar_url = "http://www.ncbi.nlm.nih.gov/clinvar/?term="+chrom_without_chr+"[chr]+AND+"+str(pos)+"[chrpos37]"
if clinvar_clinsig and all_fields:
print("Reading from: " + clinvar_url)
url_opener = urllib2.build_opener()
url_opener.addheaders = [('User-agent', "Mozilla/5.0 (X11; U; Linux i686) Gecko/20071127 Firefox/2.0.0.11")]
page_contents = url_opener.open(clinvar_url).read()
match = re.search("(\d) star.? out of maximum of 4 stars", page_contents)
if match:
number_of_stars = int(match.group(1))
else:
print("No match in page: " + clinvar_url)
for line in page_contents.split("\n"):
if "rev_stat_text hide" in line:
print(" -- this line was expected to contain number of stars: " + line)
row = map(str, [gene_name, genotype_str, variant_str, functional_class, hgvs_c, hgvs_p, rsid, exac_global_af, exac_popmax_af, exac_popmax_population, clinvar_clinsig, clinvar_clnrevstat, number_of_stars, clinvar_url, comments])
return row
def test_get_worst_vep_annotation_index(self):
annotations = [
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000479049',
'Consequence': 'non_coding_transcript_exon_variant',
'Protein_position': '',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000258104',
'Consequence': 'stop_gained',
'Protein_position': '1968',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000394120',
'Consequence': 'stop_gained',
'Protein_position': '1969',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000409366',
'Consequence': 'stop_gained',
'Protein_position': '1990',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000409582',
'Consequence': 'stop_gained',
'Protein_position': '2006',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000409651',
'Consequence': 'stop_gained',
'Protein_position': '2000',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000409744',
'Consequence': 'stop_gained',
'Protein_position': '1976',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000409762',
'Consequence': 'stop_gained',
'Protein_position': '1985',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000410020',
'Consequence': 'stop_gained',
'Protein_position': '2007',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': 'YES'
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000410041',
'Consequence': 'stop_gained',
'Protein_position': '1986',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000413539',
'Consequence': 'stop_gained',
'Protein_position': '1999',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
{
'Feature_type': 'Transcript',
'biotype': 'other',
'GMAF': '',
'Feature': 'ENST00000429174',
'Consequence': 'stop_gained',
'Protein_position': '1989',
'Gene': 'ENSG00000135636',
'STRAND': '1',
'CANONICAL': ''
},
]
# convert keys to lower case
for annot_dict in annotations:
for key, value in annot_dict.items():
annot_dict[key.lower()] = value
annot_dict['is_nc'] = False
annot_dict['is_nmd'] = False
# test basic case
self.assertEqual(get_worst_vep_annotation_index(annotations), 8)
# test 2 annotations being canonical - choose the worst one
annotations[0]['canonical'] = 'YES'
self.assertEqual(get_worst_vep_annotation_index(annotations), 8)
# test 0 annotations being canonical - choose the worst one
annotations[0]['canonical'] = ''
annotations[8]['canonical'] = ''
i = get_worst_vep_annotation_index(annotations)
self.assertTrue(annotations[i]['consequence'], 'stop_gained')
self.assertEqual(annotations[i]['feature'], 'ENST00000258104')
# test where worst-affected transcript is not the canonical one
annotations[1]['consequence'] = 'splice_donor_variant'
self.assertEqual(get_worst_vep_annotation_index(annotations), 1)
self.assertFalse(annotations[1]['canonical'])
# test protein coding filter
annotations[6]['biotype'] = 'protein_coding'
self.assertEqual(get_worst_vep_annotation_index(annotations), 6)
# test the gene_id arg
annotations[8]['canonical'] = 'YES'
annotations[1]['gene'] = 'OTHER_GENE1'
annotations[2]['gene'] = 'OTHER_GENE2'
self.assertEqual(
get_worst_vep_annotation_index(annotations, gene_id='OTHER_GENE1'),
1)
self.assertEqual(
get_worst_vep_annotation_index(annotations, gene_id='OTHER_GENE2'),
2)
annotations[8]['canonical'] = 'NO'
